{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyutils.fig as fg\n",
    "import STmodel.data.cases as stc\n",
    "import STmodel.model.st as stm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = fg.PlotConfig('manuscript_single')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load the case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = stc.case.CaseSet('Lu_2021')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## specify unburnt state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "unburnt = 'T-300_p-10_phi-0.6'\n",
    "lr = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get turbulent flame data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = case.get_case_data(unburnt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $u'/s_L^0$ in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  5., 10., 15., 20.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.turbulence_intensity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $l_t/\\delta_L^0$ in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.turbulence_length_scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $s_T/s_L$ in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.36, 12.04, 16.51, 23.17, 30.31])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.turbulent_burning_velocity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# set up model\n",
    "## get Reactant object\n",
    "use\n",
    "case.get_reactant(unburnt)\n",
    "or\n",
    "stm.Reactant(unburnt, fuel, oxidizer, chemistry, **kw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = case.get_reactant(unburnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel = case.get_fuel_stream()\n",
    "oxidizer = case.get_oxy_stream()\n",
    "chemistry = case.get_mechanism()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = stm.Reactant(unburnt, fuel, oxidizer, chemistry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Le',\n",
       " 'ReF',\n",
       " 'Ze',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " 'diffusive_thickness',\n",
       " 'laminar_flame_speed',\n",
       " 'p',\n",
       " 'sigma',\n",
       " 'stretch_factor_table',\n",
       " 'thermal_thickness',\n",
       " 'type_Le']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot $I_0$ against Ka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$I_0$')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x228.756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pc.get_simple()\n",
    "ax.plot(r.stretch_factor_table.x, r.stretch_factor_table.y)\n",
    "\n",
    "ax.set_xlabel('Ka')\n",
    "ax.set_ylabel('$I_0$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get Model object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = stm.Model(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A',\n",
       " 'B',\n",
       " 'C',\n",
       " 'C0',\n",
       " 'C_lr',\n",
       " 'Ka',\n",
       " 'Ka_def',\n",
       " 'Re',\n",
       " 'T',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " 'ratio_flame_surface',\n",
       " 'ratio_turbulent_burning_velocity',\n",
       " 'reactant',\n",
       " 'stretch_factor',\n",
       " 'turbulent_burning_velocity',\n",
       " 'type_C0']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 50\n",
    "\n",
    "ur_list = np.linspace(0, 25, num=nx)\n",
    "\n",
    "sr = np.zeros(nx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, ur in enumerate(ur_list):\n",
    "    # calculate model predicitons\n",
    "    sr[i] = m.ratio_turbulent_burning_velocity(ur, lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$s_T/s_L^0$')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x228.756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = pc.get_simple()\n",
    "\n",
    "ax.errorbar(d.turbulence_intensity,\n",
    "            d.turbulent_burning_velocity,\n",
    "            yerr=d.turbulent_burning_velocity_std,\n",
    "            fmt='o', c='b', mec='k', ms=6, mew=1,\n",
    "            capsize=3, capthick=1.5, elinewidth=1,\n",
    "            label='DNS')\n",
    "\n",
    "ax.plot(ur_list, sr, '-', c='r',label='Model')\n",
    "\n",
    "ax.set_xlim(0, 21)\n",
    "ax.set_ylim(0, 40)\n",
    "\n",
    "ax.set_xticks(np.linspace(0, 20, num=5))\n",
    "ax.set_yticks(np.linspace(0, 40, num=9))\n",
    "\n",
    "ax.legend(frameon=False)\n",
    "\n",
    "ax.text(5, 1,\n",
    "        'Lu \\& Yang, 2021\\n'\n",
    "        +r'H$_2$/Air, $p=10\\;\\mathrm{atm}$, $\\phi=0.6$')\n",
    "\n",
    "ax.tick_params(which='major', direction='in', bottom=True, top=True, left=True, right=True)\n",
    "\n",
    "ax.set_xlabel(r'$u^\\prime/s_L^0$')\n",
    "ax.set_ylabel(r'$s_T/s_L^0$')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
